{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.ensemble import BaggingRegressor, RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "boston = load_boston()\n",
    "X, y = boston.data, boston.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=2017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "n_tree = 50\n",
    "\n",
    "bag = BaggingRegressor(n_estimators=n_tree, n_jobs=-1)\n",
    "rf = RandomForestRegressor(n_estimators=n_tree, n_jobs=-1)\n",
    "\n",
    "bag.fit(X_train, y_train)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "bag_pred = np.array([bag.estimators_[tree].predict(X_test) for tree in range(n_tree)])\n",
    "rf_pred = np.array([rf.estimators_[tree].predict(X_test) for tree in range(n_tree)])\n",
    "\n",
    "bag_var = np.var(bag_pred, axis=0)\n",
    "rf_var = np.var(rf_pred, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tvDYgZxStqJFmWgMyrZAUK5RXFg2QJjKtdFoAt7AQC+1E0YoasSJ58rJuGk9M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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10caa31d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "plt.plot(range(1, len(bag_var)+1), bag_var)\n",
    "plt.plot(range(1, len(rf_var)+1), rf_var)\n",
    "plt.legend(['bag_var', 'rf_var'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bagging variance average: 15.121\n",
      "random forest variance average: 14.311\n"
     ]
    }
   ],
   "source": [
    "print 'bagging variance average:', round(np.mean(bag_var), 3)\n",
    "print 'random forest variance average:', round(np.mean(rf_var), 3)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}